Σύγκριση μεθόδων

	Κ-Κοντινότεροι Γείτονες	Λογιστική Παλινδρόμηση	Naive Bayes	Τυχαίο Δάσος
Πεδίο	Μηχανική Μάθηση	Ερευνητική Στατιστική	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Process / pipeline	Machine learning	Machine learning
Έτος προέλευσης	1967	1958	1997	2001
Δημιουργός	Cover, T.M. & Hart, P.E.	David Roxbee Cox	Mitchell, T. M. (textbook treatment)	Breiman, L.
Τύπος	Instance-based (non-parametric) learning	Method	Probabilistic classifier (Bayes' theorem with conditional independence)	Ensemble (bagging of decision trees)
Θεμελιώδης πηγή	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Εναλλακτικές ονομασίες	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	logit model, binomial logistic regression, LR	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Συναφείς	5	3	4	4
Σύνοψη	K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.